Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

Objectives In recent years, research on the use of electronic noses (e-nose) has developed rapidly, especially in the medical and food fields. Typically, e-nose is coupled with machine learning algorithms to detect or predict multiple sensory classes in a given sample. In many cases, comprehensive and complete experiments are required to ensure the generalizability of the predictive model. For this reason, homogeneous data sets are important to use. Homogeneous data sets refer to the data sets obtained from different observations in almost similar environmental condition. In this data article, e-nose homogeneous data sets are provided for beef quality classification and microbial population prediction. Data description This data set is originated from 12 type of beef cuts. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors for 2220 min. The formal standards, issued by the Meat Standards Committee, are used as a reference in labeling beef quality. Based on the number of microbial populations, meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled. The data set is formatted in "xlsx" file. Each sheet represents one beef cut. Moreover, data sets are good cases for feature selection algorithm stability test, especially to solve sensor array optimization problems.


Objective
In recent years, research on the use of the e-nose has developed, especially in the medical and food fields. Typically, e-noses are combined with machine learning algorithm to detect or predict several sensory classes in a particular sample. Hence, e-nose signal processing becomes an essential part of e-nose systems. In many cases, comprehensive and complete experiments are required to make sure the generalization of prediction model. Even though they have almost the same pattern, the combination of several experiments shows that there are differences in the homogeneous data sets. Based on several machine learning algorithms, experimental results show different scores with different cuts of beef [1]. This difference can be influenced by the composition or mixture of samples (protein, lipid, etc.) and also environmental factors, for example temperature and humidity. Using a comprehensive data set is one of the ways needed to build a predictive model that has good generalization. In this data article, homogeneous data sets refer to the data sets collected from different samples in almost similar environmental conditions. The homogeneous data sets are suitable for developing and testing the generalizability of machine learning models [2]. The availability of homogeneous data sets will provide a more comprehensive pattern, especially regarding the assessment of beef quality with various types of beef cuts

BMC Research Notes
*Correspondence: dedyrw@telkomuniversity.ac.id compared to other data sets [3]. Moreover, these data sets are good cases for feature selection algorithm (FSA) stability tests [4], especially to overcome sensor array optimization problems in e-nose [5].

Data description
This data set is originated from 12 type of beef cuts such as tenderloin, striploin (shortloin), top sirloin, brisket, rib eye, skirt meat (plate), round (shank), inside/outside, chuck/ clod, fat, shin, and flank (flap meat). The process of beef spoilage is recorded using 11 gas sensors of Metal-Oxide Semiconductor (MOS) for 2220 min. The data set is formatted in "xlsx" file. Each sheet represents one beef cut which is a contained column as follows [6]: • Minute: time in minute • TVC: continuous label in the total viable count (microbial population) • Label: discrete label, 1,2,3,4 denote "excellent", "good", "acceptable", and "spoiled", respectively. • MQ_: the resistant value of gas sensors. Table 1 shows the overview of data sets. Formal standards, issued by the Meat Standards Committee of Agricultural and Resource Management Council of Australia and New Zealand (ARMCANZ), are used for labeling beef quality [7]. The optical density (600 nm) is measured by a Spectrophotometer (Genesys 20), and the number of cells is calculated by a hemocytometer. Classical and twohour methods were applied in the experiment [8]. Based on the number of microbial populations (log 10 cfu/g), meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled.
The data sets were generated from the experimental results from the decomposition of several types of beef cuts recorded using a prototype mobile e-nose device at a stable temperature (Data file 1). A list of components used in the experiment can be seen in "Component list for experiment.xlsx". The experiments are carried out with fresh meat from a shop or market with the assumption that the meat has the best level of freshness (excellent). The experiment was carried out at a controlled temperature (± 29 °C) with the aim of making the response of the gas sensor more stable. The experimental scheme at controlled temperature can be seen in Data file 3 [6]. The data generated by the gas sensor array in the sample chamber will be sent to the server automatically every minute to see the condition of the meat for 2-3 days non-stop. The weight of the meat tested was 125 g which were placed in the sample chamber for approximately 2 days until it reached a spoiled condition. In the experiment, the type of meat will also be considered based on the fat composition in the cut of meat. This data set is originated from 12 beef cuts types such as tenderloin, striploin (shortloin), top sirloin, brisket, rib eye, skirt meat (plate), round (shank), inside/outside, chuck/ clod, fat, shin, and flank (flap meat) as shown in Data file 4 to Data file 15 [6].

Limitations
These data sets are collected at controlled temperatures. However, humidity is uncontrolled and naturally affected by the air vapor from the meat decomposition process which may affect noise contamination in the data sets.